Most of the actions taken within today’s power plants are directed
by control systems, which usually are computerised and located in a central
control room within the power plant. In normal states, the communication
between the control system and the operators is satisfactory, with few
alarms occurring infrequently. However, when large disturbances occur, the
communication is problematical. Instead of being aided by the messages, the
operators become swamped by the amount of information, and often have to
make more or less informed guesses of what causes the abnormal situation. It
is therefore of great importance if the control system can discriminate
between normal and abnormal situations, as well as being less sensitive and
giving priority to alarms that must be sent to the operators. In order for
the system to make such analyses, processes for diagnosis and decision
making regarding the reliability and importance of the information are
needed. This paper shows how machine learning algorithms can be combined
with decision theory w.r.t. vague and numerically imprecise background
information, by using classifiers. An ensemble is a classifier created by
combining the predictions of multiple component classifiers. We present a
new method for combining classifiers into an ensemble based on a simple
estimation of each classifier’s competence. The purpose is to develop a
filter for handling complex alarm situations. Decision situations are
evaluated using fast algorithms developed particularly for solving these
kinds of problems. The presented framework has been developed in
co-operation with one of the main actors in the Swedish power plant
industry.

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